Numerical data processing dedicated to an integrated microspetrometer

ABSTRACT

A method and apparatus for obtaining higher resolution spectral data on lower-resolution spectral data provided by a spectrometric transducer is described. The method includes calibrating the spectrometric transducer to produce results for a reconstruction of spectra using the results, and processing the lower-resolution spectra data using a set of numerical algorithms dedicated to an integrated micro-spectrometers associated with the spectrometric transducer and using the results of calibrating to provide the higher resolution spectra data. The apparatus utilizes the method.

This application is a continuation of U.S. patent application Ser. No. 11/071,884 filed on Mar. 4, 2005 which claims priority from U.S. Provisional Patent Application No. 60/549,562 filed on Mar. 4, 2004.

BACKGROUND OF THE INVENTION

A miniature lowest integrated spectrophotometer (an integrated microspectrometer) may be used in a number of applications, such as industrial monitoring and quality control, medical and pharmaceutical testing, plant growth characterization, environmental and pollution monitoring, food control, and light source testing. Process control, such as in chemical plants, in the semiconductor industry, in dye works and in electroplating operations, is another area taking advantage of higher spectral resolution and measurement accuracy of spectrophotometry. Other applications that may require simultaneous measurement of several spectra include multiangle color measurement of paint work, continual real-time monitoring of water quality, color control in color printers and digital cameras, olive-oil and wine quality assessment, non-invasive blood glucose detection, fruits analysis, cosmetics and drug analysis, and security-related equipment.

Spectrophotometry may be considered as an analytic technique concerned with the measurement and characterization of the interaction of light energy with matter. Spectrophotometry may involve working with instruments designed for this purpose, referred to by some as spectrophotometers, and corresponding methods of interpreting the interaction both at the fundamental level and for practical analysis. The distribution of light energy, absorbed or emitted by a sample of a substance under study, may be referred to as its spectrum. If energy of ultraviolet (UV), visible (Vis) or infrared (IR) light is used, the corresponding spectrum may be referred to as a light spectrum.

A spectrophotometer may have a resolution associated with its design or implementation affecting resolution of measured spectra. As is well understood by those of skill in the art of spectrometry, a required resolution for UV and a required resolution for IR spectral imaging may be different. Further, the terms high-resolution and low-resolution are related to an imaged spectral band or to wavelengths of light within the imaged band. For a broadband spectrometer, either graduated spectral resolution or a spectral resolution sufficient to properly image each band may be used.

Interpretation of spectra may provide fundamental information at atomic and molecular energy levels. For example, the distribution of species within those levels, the nature of processes involving change from one level to another, molecular geometries, chemical bonding, and interaction of molecules in solution may all be studied using spectrum information. Comparisons of spectra to provide a basis for the determination of qualitative chemical composition and chemical structure, and quantitative chemical analysis is described in detail in Parker S. (Ed.): McGraw-Hill Encyclopedia of Chemistry, McGraw-Hill, 1983, which is hereby incorporated by reference.

Known techniques for processing data from spectrum analyzers tend to provide inadequate quality of spectrum estimation for lower-cost spectrophotometers.

U.S. Pat. No. 5,712,710 for example, describes a probe for use in measuring the concentration of a specific metal ion dissolved in liquid. The device suffers from known problems of probe miniaturization. Either the bandwidth of the spectrometer is narrow to accommodate a small probe size, the quality of the spectral imaging is poor, or the optical processing components are large and costly. The described device comprises a hand-held processing unit coupled to the probe. The processing unit is programmed to calculate and display the concentration of a specific material. In this probe neither the photodetector nor the processing unit is integrated with the light diffraction structure. Further, the use of poor resolution in imaging the spectrum tends to be unacceptable for most applications when using such a probe.

U.S. Pat. No. 5,020,910 describes a method of forming a light diffraction structure directly over a photodetector. The described device requires external electronic circuitry to obtain a useful spectrum of light and the spectral resolution is very high in comparison to that of existing conventional spectrometers.

U.S. Pat. No. 5,731,874 describes a spectrometer with an integrated photodetector. The described device is sensitive only to particular spectral lines and thus tends to be useful only over a narrow spectral range.

Therefore, there exists a need for a method and apparatus to alleviate some disadvantages in the prior art.

Known laboratory spectrophotometers may perform acceptably for some applications, but they are typically bulky and costly. It would be desirable to provide lower-cost integrated micro-spectrophotometer capable of determining the spectral characteristics of the optical signals

SUMMARY OF THE INVENTION

In a broad aspect of the present invention, there is a family of integrated microspectrometers whose principle of functioning is based on the subsequent use of a spectrometric transducer and a computing circuit, including a digital signal processor.

In another broad aspect of the present invention, there is provided a method for obtaining higher-resolution spectral data on the lower-resolution spectral data provided by the spectrometric transducer. The method comprises a set of numerical algorithms, dedicated to integrated microspectrometers of this type, designed for calibration of those devices and for reconstruction of spectra using the results of calibration.

In another broad aspect of the present invention, there is provided a method for obtaining higher resolution spectral data on lower-resolution spectral data provided by a spectrometric transducer. The method comprises: calibrating the spectrometric transducer to produce results for a reconstruction of spectra using the results; and processing the lower-resolution spectra data using a set of numerical algorithms dedicated to an integrated micro-spectrometers associated with the spectrometric transducer and using the results of calibrating to provide the higher resolution spectra data.

BRIEF DESCRIPTION OF THE DRAWINGS

An embodiment of the present invention will now be described by way of example only with reference to the accompanying drawings, in which:

FIG. 1 is a schematic diagram of an optical spectrum measurement instrument.

FIG. 2 is a flow diagram of a procedure for resampling g_(n)(λ).

FIG. 3 is a flow diagram of a procedure for obtaining the final result of a spectrum estimation.

DETAILED DESCRIPTION OF THE INVENTION

The description which follows, and the embodiments described therein, are provided by way of illustration of an example, or examples, of particular embodiments of the principles of the present invention. These examples are provided for the purposes of explanation, and not limitation, of those principles and of the invention. In the description, which follows, like parts are marked throughout the specification and the drawings with the same respective reference numerals.

A way to provide lower-cost integrated micro-spectrophotometer capable of determining the spectral characteristics of the optical signals is harness more of the computing power of microprocessors and other processors. Aspects of such a design paradigm shift is described in U.S. Pat. No. 6,002,479 Apparatus and Method for Light Spectrum Measurement and U.S. Pat. No. 5,991,023 Method of Interpreting Spectrometric Data, the specifications of which are hereby incorporated by reference. Specific numerical techniques may be applied in utilizing the computational power of processors to ensure a required precision and high dynamic range of a spectrophotometer instrument as described below.

Although spectrometric transducers are used in a variety of devices, for the purpose of the following discussion it will be described in the context of an integrated microspectrometer. As illustrated in FIG. 1, an integrated microspectrometer 10 basically consists of a spectrometric transducer 20 and a digital signal processor (DSP) 30. The spectrometric transducer 20 converts an optical signal 12 into a digital signal {tilde over (y)} representative of the spectrum x(λ) 15 of that optical signal 12. The spectrometric transducer 20 receives an analog optical input 24 and provides an output through N digital electrical outputs, typically photodiodes that convert an incident optical signal into a corresponding electrical signal. In practice, the spectrometric transducer 20 could be a dedicated optoelectronic transducer or a complete instrument such as a spectrum analyzer with fixed measurement parameters such as wavelength range, optical resolution, sensitivity, etc.

Assumptions and Notations

The spectrometric transducer 20 output-related discretization of the wavelength axis is defined by the sequence {λ_(n)} such that λ_(min)=λ₁<λ₂< . . . λ_(N−1)<λ_(N)=λ_(max)  Equation 1 where N is the number outputs of the spectrometric transducer 20. Thus, the average interval between the consecutive wavelength values is: ${\Delta\lambda} = \frac{\lambda_{\max} - \lambda_{\min}}{N - 1}$

The spectrometric transducer 20 calibration-related discretization of the wavelength axis is defined by the formula: λ′_(m)=λ′_(min)+(m−1)Δλ′ m=1, . . . , M  Equation 2 where M>>N is the number of discretization points, i.e. of the position of the tunable monochromators used for calibration, and: ${\Delta\lambda}^{\prime} = \frac{\lambda_{\max}^{\prime} - \lambda_{\min}^{\prime}}{M - 1}$ with  λ_(min)^(′) ≅ λ_(min)  and  λ_(max)^(′) ≅ λ_(max).

It is assumed that the spectrum x(λ) 15 of the analyzed optical signal 12, i.e. light intensity vs. wavelength, may be adequately approximated by a known function {circumflex over (x)}(λ; p) with unknown parameters p=[p₁ . . . p_(K)]^(T). A sequence of samples of x(λ) 15 is a particular case of such a vector (due to the Shannon theorem). In this case: $\begin{matrix} {{{p \equiv x} = {\left\lbrack {x_{1}\ldots\quad x_{K}} \right\rbrack^{T} \equiv \left\lbrack {{x\left( \lambda_{1}^{''} \right)}\ldots\quad{x\left( \lambda_{K}^{''} \right)}} \right\rbrack^{T}}}{{where}\text{:}}{{\lambda_{k}^{''} = {{\lambda_{\min}^{''} + {\left( {k - 1} \right)\Delta\quad\lambda^{''}\quad{for}\quad k}} = 1}},\ldots\quad,{K{with}{\lambda_{\min}^{''} \cong {\lambda_{\min}\quad{and}\quad\lambda_{\max}^{''}} \cong \lambda_{\max}}},\quad{{{and}\text{:}\Delta\quad\lambda^{''}} = {\frac{\lambda_{\max}^{''} - \lambda_{\min}^{''}}{K - 1}.}}}} & {{Equation}\quad 3} \end{matrix}$ Mathematical Model of the Data

An adequate implicit model of the data {{tilde over (y)}_(n)|n=1, . . . , N} 25 acquired at the output of the spectrometric transducer 20 has the form: $\begin{matrix} {{{F\left( {{\hat{y}}_{n};\alpha_{n}} \right)} = {\int_{{- \Delta}\quad{\lambda/2}\quad\rho}^{{+ \Delta}\quad{\lambda/2}\quad\rho}{\left\lbrack {\int_{- \infty}^{\infty}{{g\left( {{\lambda_{n} + \lambda^{\prime}},\lambda} \right)}{x(\lambda)}{\mathbb{d}\lambda}}} \right\rbrack{\mathbb{d}\lambda^{\prime}}}}}{{{{for}\quad n} = 1},\ldots\quad,N}} & {{Equation}\quad 4} \end{matrix}$ where F(∞; α_(n)) is a known function with unknown parameters α_(n)=[α₁ . . . α_(Q)]^(T), that models the inverse “static” characteristic of the spectrometric transducer 20 with respect to the n th photodiode (n=1, . . . , N); g(λ′, λ) is the impulse response of the optical part of the spectrometric transducer 20, and ρ is the ratio of the distance between two consecutive photodiodes and the photodiode width. The above model may be given a simpler form: $\begin{matrix} {{F\left( {{\hat{y}}_{n};\alpha_{n}} \right)} = {{\int_{- \infty}^{+ \infty}{{g_{n}\left( {\lambda_{n} - \lambda} \right)}{x(\lambda)}{\mathbb{d}\lambda}}}\quad{{{{for}\quad n} = 1},\ldots\quad,N}}} & {{Equation}\quad 5} \end{matrix}$ by introducing the functions: $\begin{matrix} {{{g_{n}(\lambda)} = {\int_{{- \Delta}\quad{\lambda/2}\quad\rho}^{{+ \Delta}\quad{\lambda/2}\quad\rho}{{g\left( {{\lambda_{n} + \lambda^{\prime}},{\lambda_{n} - \lambda}} \right)}{\mathbb{d}\lambda^{\prime}}}}}{{{{for}\quad n} = 1},\ldots\quad,N}} & {{Equation}\quad 6a} \end{matrix}$ being the normalized responses of the photodiodes. Each of these functions is the response of the spectrometric transducer 20, measured at the output of the nth photodiode, to a sweeping monochromator producing an optical signal whose spectrum may be adequately modeled with x(λ)≡δ(λ−l) where l is moving from λ_(min) to λ_(max). This response is assumed to be centered around λ=0, and—thus—satisfying the condition: $\begin{matrix} {{{\int_{- \infty}^{0}{{g_{n}(\lambda)}{\mathbb{d}\lambda}}} = {{\int_{0}^{+ \infty}{{g_{n}(\lambda)}{\mathbb{d}\lambda}}} = 0.5}}{{{{for}\quad n} = 1},\ldots\quad,N}} & {{Equation}\quad 6b} \end{matrix}$

Under this assumption, the response of the model to the optical signal with a flat spectrum (i.e. constant as x varies), x(λ)=X^(f), satisfies the following equations: F(ŷ _(n); α_(n) ^(f))=X ^(f) for n=1, . . . , N  Equation 7 and its response to a monochromatic signal, x(λ)=Xδ(λ−l), the equations: F(ŷ _(n); α_(n))=Xg _(n)(λ_(n) −l) for n=1, . . . , N  Equation 8 Calibration of the Spectrometric Transducer

Numerical data processing dedicated to the integrated microspectrometer 10 comprises reference data processing aimed at calibration of the spectrometric transducer 20, and estimation of the spectrum of an analyzed optical signal or of its parameters. Calibration-related data processing may be performed by an external computer, while estimation of parameters must rely on the internal DSP 30.

It follows from Equation 7 that the parameters α_(n) corresponding to the n th wavelength (n=1, . . . , N) may be estimated during calibration from the responses {{tilde over (y)}_(n,q) ^(f,cal)} of the spectrometric transducer 20 to Q flat-spectrum signals: x(λ)=x _(q) ^(f,cal)(λ)≡X _(q) ^(f) for q=1, . . . ,Q  Equation 9 by solving a set of algebraic equations: {circumflex over (α)}_(n) =arg _(α) {F({tilde over (y)} _(n,q) ^(f,cal);α)=X _(q) ^(f) |q=1, . . . ,Q} n=1, . . . ,N  Equation 10 Accordingly, α may be obtained for each n.

On the other hand, the estimation of the functions g_(n)(λ) could be based on the reference data: {{tilde over (y)}_(n,m) ^(cal)|n=1, . . . ,N}  Equation 11 acquired at the spectrometric transducer 20 output excited by a broadband source followed by a tunable monochromator: x _(m) ^(cal)(λ)=X _(m)δ(λ−λ′_(m)) for m=1, . . . ,M  Equation 12

If the data are normalized before processing: {tilde over (y)} _(n,m) ^(cal) F({tilde over (y)} _(n,m) ^(cal);{circumflex over (α)}_(n))/X _(m) for n=1, . . . ,N; m=1, . . . ,M  Equation 13 then their model defined by Equation 8 takes on the form: ŷ _(n,m) ^(cal) =g _(n)(λ_(n)−λ_(m)) for n=1, . . . , N; m=1, . . . ,M  Equation 14

This formula suggests that, for any fixed value of n, one may obtain a discrete representation of g_(n)(λ) by direct smoothing of the data {{tilde over (y)}_(n,M−m+1) ^(cal)|m=1, . . . ,M}: ĝ _(n,m) ≈g _(n)(λ_(n)−λ_(M−m+1)) for m=1, . . . ,M  Equation 15

This sequence has to be properly centered by estimation of λ_(n). First, the interval └λ_(M−m*),λ_(M−m*+1)┘, the wavelength λ_(n) belongs to, should be identified: $\begin{matrix} {{m^{*} = {\arg_{m}\left\{ {S_{n,m} \leq {0.5\quad S_{n,m_{\max}}} \leq S_{n,{m + 1}}} \right\}}}{{with}\text{:}}} & {{Equation}\quad 16} \\ {S_{n,m} = {\sum\limits_{\mu = m_{\min}}^{m}{\hat{g}}_{n,\mu}}} & {{Equation}\quad 17} \end{matrix}$ where m_(min) and m_(max) are indices delimiting the central part of the main lobe of the estimate {ĝ_(n,m)} of the spectrometric transducer 20 response corresponding to the n th wavelength value. Then, an estimate {circumflex over (λ)}_(n) of λ_(n) may be found by means of linear interpolation based on two points:

λ_(M−m*),S_(n,m*+1)

and

λ_(M−m*+1),S_(n,m*)

  Equation 18

The result of this interpolation is: $\begin{matrix} {{{{centre}\quad{of}\quad g\quad{\hat{\lambda}}_{n}} = {{w_{n}\lambda_{M - m^{*}}} + {\left( {1 - w_{n}} \right)\lambda_{M - m^{*} + 1}}}}{{with}\text{:}}} & {{Equation}\quad 19} \\ {w_{n} = \frac{S_{n,{m^{*} + 1}} - {0.5\quad S_{n,m_{\max}}}}{{\hat{g}}_{n,{m^{*} + 1}}}} & {{Equation}\quad 20} \end{matrix}$ which represents the centre of the response for a given output. As noted above, the calibration α, to obtain α and g may be done on a computer. Estimation of the Spectrum and/or its Parameters

Using the results of the calibration, it is then possible to estimate spectrum using the DSP 30. It is assumed that the data for parameter estimation are normalized in the following way before processing: {tilde over (z)} _(n) =F({tilde over (y)} _(n);{circumflex over (α)}_(n)) for n=1, . . . ,N  Equation 21

Consequently, the problem of spectrum estimation to be solved is non-stationary but linear, and the parameterized model of the normalized data, resulting from of Equation 5, assumes the form: {circumflex over (z)} _(n)(p)=[g_(n)(λ)*{circumflex over (x)}(λ;p)]_(λ=λ) _(n) for n=1, . . . ,N  Equation 22

A wide class of variational methods for spectrum estimation may be defined using this model: {circumflex over (p)}=arg _(p) inf{∥{tilde over (z)}−{circumflex over (z)}(p)∥_(w) |pεP} with q=2 or ∞  Equation 23 where: {tilde over (z)}=[{tilde over (z)}₁ . . . {tilde over (z)}_(N)]^(T)  Equation 24 {circumflex over (z)}(p)=[{circumflex over (z)} ₁(p) . . . {circumflex over (z)} _(N)(p)]^(T)  Equation 25 and P is a set of optimization constraints.

Numerous sequential filtering algorithms may be directly applied or adapted for solving the problem of spectrum estimation on the basis of raw measurement data modeled by Equation 22, in particular as described in the attached bibliography:

-   -   direct methods [1], [2], [3], [4], [5];     -   spline-based recursive methods [6], [11];     -   Kalman-filter-based methods [7], [8], [9], [10], [11];     -   polynomial-filter-based methods [12], [13], [14], [15];     -   rational-filter-based methods [16], [17], [18];     -   Cauchy-filter-based and neural-network-based methods [19], [20],         [21].

However, under certain conditions, deconvolution methods may be also applied for this purpose. Three viable options will now be described: transformation of the wavelength axis, interpolation of multiple solutions and linear stationary solution.

Transformation of the Wavelength Axis

If the functions g_(n)(λ) differ in height to width ratio but do not differ significantly in shape, then deconvolution methods may be used for the estimation of the spectrum after appropriate transformation of the λ-axis: λ=T(λ) for λε[λ_(min), λ_(max)]  Equation 26

This transformation should be based on the height-to-width ratios characterizing those functions: r₁, . . . , r_(N), and should convert a non-stationary problem of spectrum reconstruction into a stationary one; thus, it should satisfy the following conditions: T′(λ_(n))=ar _(n) +b for n=1, . . . , N  Equation 27 T(λ₁)=ar ₁ +b  Equation 28 T(λ_(N))=ar _(N) +b  Equation 29

Assuming that T(λ) is a known function, e.g. a spline function, with N free parameters, the above set of N+2 algebraic equations can be solved with respect to a, b, and those parameters. Consequently, the set of transformed wavelength values can be determined: λ _(n) =T(λ_(n)) for n=1, . . . ,N  Equation 30

They are necessary for resampling of the functions g_(n)(λ) and computing { g _(m)}, which is the average of the resampling results. It should be noted that the dispersion in the resampled functions may be partially compensated for by appropriate adjustment of the function F({tilde over (y)}_(n);{circumflex over (α)}_(n)).

The procedure for resampling g_(n)(λ), represented by a sequence {g_(n,m)|m= . . . , −1, 0, 1, . . . } is depicted by the flow chart shown in FIG. 2. The sequence of steps composing the procedure is indicated by the sequence of blocks 42 to 46.

In block 42 the procedure starts by determining g _(n)( λ) by smoothing approximation or interpolation of the sequence {g_(n,m), λ _(n+m)|m= . . . ,−1,0,1, . . . }.

Then at block 44, { g _(n,m)= g _(n)( λ _(n)+mΔλ)|m= . . . −1,0,1, . . . } is computed.

Following which, at block 46, $\left\{ {{{\overset{\_}{g}}_{m} = {\left. {\frac{1}{N}{\sum\limits_{n = 1}^{N}{\overset{\_}{g}}_{n,m}}} \middle| m \right. = \ldots}}\quad,{- 1},0,1,\ldots} \right\}$ is computed.

Assuming that the sequence { x _(n)|n=1, . . . ,N} is the result of a deconvolution, obtained on the basis of {{tilde over (y)}_(n)} and {{tilde over (g)}_(m)}, the procedure for obtaining the final result of the spectrum estimation is depicted by the flow chart shown in FIG. 3. The sequence of steps composing the procedure is indicated by the sequence of blocks 52 to 54.

In block 52 the procedure starts by determining {circumflex over (x)}( λ) by smoothing approximation or interpolation of the sequence { x _(n), λ _(n)|n=1, . . . , N}.

Then, at block 54, {{circumflex over (x)}_(n)={circumflex over (x)}(λ_(n))|n=1, . . . ,N} is computed.

Interpolation of Multiple Solutions

An alternative methodology enabling the use of deconvolution algorithms for solving non-stationary problems of spectrum reconstruction may be based on the use of different responses g_(i)(λ) for processing the data in consecutive overlapping intervals.

Assuming that:

-   -   each interval contains ΔN data;     -   the intervals are determined by the indices N_(i+1)=N_(i)+ΔN;     -   g_(i)(λ) is the response used for deconvolution for n= . . .         ,N_(i−1), . . . N_(i), . . . , N_(i+1), . . . ;     -   {{circumflex over (x)}_(n) ^((i))} is the result of         deconvolution performed with g_(i)(λ).

Then the linearly-interpolated final result of the deconvolution for n=N_(i−1), . . . N_(i), . . . ,N_(i+1) may be calculated according to the formula: $\begin{matrix} {{\hat{x}}_{n} = \left\{ \begin{matrix} {{\frac{N_{i} - n}{\Delta\quad N}{\hat{x}}_{n}^{({i - 1})}} + {\frac{n - N_{i - 1}}{\Delta\quad N}{\hat{x}}_{n}^{(i)}}} & {{{{{for}\quad n} = {N_{i - 1} + 1}},}{...\quad,N_{i}}} \\ {{\frac{N_{i + 1} - n}{\Delta\quad N}{\hat{x}}_{n}^{(i)}} + {\frac{n - N_{i}}{\Delta\quad N}{\hat{x}}_{n}^{({i + 1})}}} & {{{{{for}\quad n} = {N_{i} + 1}},}{...\quad,{N_{i}.}}} \end{matrix} \right.} & {{Equation}\quad 31} \end{matrix}$ Linear Stationary Solution

The algorithms dedicated to spectrometric transducers of relatively high resolution may be developed taking into account that the imperfections of a monochromator and of a reference spectrometer used for calibration cannot be neglected. On the other hand, those algorithms may be based on the assumption that neither g_(n)(λ) nor α_(n), in Equation 5 depend on n. If, moreover, the assumption of the linear function F(∞; α_(n)) is justified, then the mathematical model of the spectrometric transducer 20 and the mathematical model of the chosen reference spectrometer take on the form: {circumflex over (z)} _(n)≡α₀+α₁ ŷ _(n) =[g(λ)*x(λ)]_(λ=λ) _(n) for n=₁, . . . , N  Equation 32 {circumflex over (z)} _(R,n)≡α_(R,0)+α_(R,1) ŷ _(R,n) =[g _(R)(λ)*x*λ)]_(λ=λ) _(n) for n=1, . . . , N.  Equation 33

By representing g(λ) as a convolution of g_(R)(λ) and an auxiliary function g_(Δ)(λ): g(λ)=g _(R)(λ)*g _(Δ)(λ)  Equation 34 the relationship between the two models becomes: {circumflex over (z)} _(n) =[g _(Δ)(λ)*z _(R)(λ)]_(λ=λ) _(n) for n=1, . . . ,N  Equation 35 where: z _(R)(λ)=g _(R)(λ)*x(λ).  Equation 36

Thus, the algorithm of spectrum reconstruction will be a numerical implementation of the following deconvolution formula: {circumflex over (x)}(λ)= DECONV[{{circumflex over (α)}₀+{circumflex over (α)}₁ {tilde over (y)} _(n) },ĝ _(Δ)(λ)]  Equation 37 where: {circumflex over (α)}₀, {circumflex over (α)}₁, and ĝ_(Δ)(λ) are the estimates of α₀, α₁, and g_(Δ)(λ), obtained during the calibration of the spectrometric transducer 20.

The static calibration of the spectrometric transducer 20 requires the use of two flat-spectrum signals: x₁ ^(f,cal)(λ)≡X₁ ^(f) and x₂ ^(f,cal)(λ)≡X₂ ^(f).  Equation 38

Assuming that X₂ ^(f)=0, the estimates of α₀, and α₁ may be obtained as: $\begin{matrix} {{{\hat{\alpha}}_{1} = {{\frac{X_{1}^{f}}{{\overset{\_}{y}}_{1}^{f,{cal}} - {\overset{\_}{y}}_{2}^{f,{cal}}}\quad{and}\quad{\hat{\alpha}}_{0}} = {{- {\hat{\alpha}}_{1}}{\overset{\_}{y}}_{2}^{f,{cal}}}}}{{where}\text{:}}} & {{Equation}\quad 39} \\ {{\overset{\_}{y}}_{1}^{f,{cal}} = {{\frac{1}{N}{\sum\limits_{n = 1}^{N}{{\overset{\sim}{y}}_{n,1}^{f,{cal}}\quad{and}\quad{\overset{\_}{y}}_{2}^{f,{cal}}}}} = {\frac{1}{N}{\sum\limits_{n = 1}^{N}{{\overset{\sim}{y}}_{n,2}^{f,{cal}}.}}}}} & {{Equation}\quad 40} \end{matrix}$

The dynamic calibration of the spectrometric transducer 20 requires at least one quasi-monochromatic signal x₁ ^(cal) (λ) and the acquisition of the corresponding reference data at the output of the spectrometric transducer 20 and at the output of the reference spectrometer: {{tilde over (y)}_(n,1) ^(cal)} and {{tilde over (y)}_(R,n,1) ^(cal)}. The estimate of g(λ) may then be determined by means of an algorithm being a numerical implementation of the following deconvolution formula: ĝ(λ)=DECOQNVW[{{circumflex over (α)} ₀+{circumflex over (α)}₁ ^(cal)},{{circumflex over (α)}_(R,0)+{circumflex over (α)}_(R,1) {tilde over (y)} _(R,n,1) ^(cal)}]  Equation 41 where {circumflex over (α)}_(R,0) and {circumflex over (α)}_(R,1) are to be determined in the same way as {circumflex over (α)}₀ and {circumflex over (α)}₁ on the basis of the responses of the reference spectrometer to two flat-spectrum signals. Global Optimization of the Microspectrometer Performance

Assuming that the spectrum x(λ) 15 is parameterized by equidistant sampling, as defined by Equation 3: $\begin{matrix} {{x(\lambda)} \cong {\sum\limits_{v = 1}^{N}{x_{v}{\sin c}\quad{\left( {\pi\frac{\lambda - \lambda_{v}}{\Delta\lambda}} \right).}}}} & {{Equation}\quad 42} \end{matrix}$

Consequently, the normalized model of the data defined by Equation 25 takes on the form: $\begin{matrix} {{\hat{z} = {Gx}}{{where}\text{:}}} & {{Equation}\quad 43} \\ {{G_{n,k} = {\int_{- \infty}^{+ \infty}{{{g_{n}\left( {\lambda_{n} - \lambda^{\prime}} \right)} \cdot \sin}\quad c\quad\left( {\pi\frac{\lambda^{\prime} - \lambda_{k}^{''}}{{\Delta\lambda}^{''}}} \right){\mathbb{d}\lambda^{\prime}}}}}{{{{for}\quad n} = 1},\ldots\quad,{N;}}{{k = 1},\ldots\quad,{K.}}} & {{Equation}\quad 44} \end{matrix}$

It follows from this model that the attainable accuracy of spectrum estimation wilt mainly depend onpon three factors:

-   -   the uncertainty Δ{tilde over (z)} of the vector {tilde over (z)}         whose elements are defined by Equation 21;     -   the uncertainty ΔG of the matrix G whose elements, defined by         Equation 44 are subject to the errors resulting from measurement         identification of the functions g_(n)(λ);     -   the conditioning number of the matrix G: cond(G)=∥G∥         G⁻¹         .

The following inequality characterizes this dependence: $\begin{matrix} {\frac{{\Delta\quad x}}{x} \leq {\frac{{cond}\quad(G)\frac{{\Delta\quad G}}{G}}{1 - {{cond}\quad(G)\frac{{\Delta\quad G}}{G}}} + {{cond}\quad(G)\frac{{\Delta\quad\overset{\sim}{z}}}{\overset{\sim}{z}}}}} & {{Equation}\quad 45} \end{matrix}$ provided

G⁻¹

∥ΔG∥<1. (for any consistent pair of vector and matrix norms).

The right-hand side of the inequality defined by Equation 45 should be taken into account as a criterion for global optimization of spectrometric transducer 20 design.

INDUSTRIAL APPLICABILITY

It should be noted that the presented general methodology of numerical data processing, dedicated to the integrated microspectrometer 10, takes into account both random and systematic imperfections of the spectrometric transducer 20, including its nonlinearity and variability of its optical responses along the wavelength axis. This variability is of particular importance if the so-called multi-order effects in the optical part of the spectrometric transducer 20 cannot be neglected. Their presence may be modeled by the multimodal functions g_(n)(λ), i.e. the functions having more than one maximum. The principal order is represented by a zero-centered peak, and higher and lower orders are represented by additional peaks whose positions are varying with the wavelength (n). The only change implied by this fact is the more complicated structure of the matrix G in Equation 43, sometimes resulting in worse ill-conditioning of the problem of spectrum reconstruction.

The following references address aspects of one or more embodiments described herein, and are hereby incorporated by reference:

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Although the present invention has been described by way of a particular embodiment an example thereof, it should be noted that it will be apparent to persons skilled in the art that modifications may be applied to the present particular embodiment without departing from the scope of the present invention. 

1. A method for obtaining higher resolution spectral data on lower-resolution spectral data provided by a spectrometric transducer, comprising: calibrating the spectrometric transducer to produce results for a reconstruction of spectra using the results; processing the lower-resolution spectra data using a set of numerical algorithms dedicated to an integrated micro-spectrometers associated with the spectrometric transducer and using the results of calibrating to provide the higher resolution spectra data. 